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In the absence of a runaway choice, there's an ever-growing push among traditional media members in favor of Derrick Rose's MVP candidacy -- and to be totally honest, the advanced boxscore-based stats don't see it. Rose is having a tremendous season, without a doubt, but he's currently 9th in Win Shares, 17th in WS per 48 minutes, 14th in Player Efficiency Rating, and 14th in Statistical +/-... Not exactly the most impressive MVP resume from the stathead's perspective.

However, there is one advanced metric that does validate the love for Rose: Adjusted Plus/Minus (via BasketballValue.com). Sure, the standard errors are huge, and Mike Dunleavy Jr. shows up as the 2nd-best player behind Rose (yikes!). But at least there is some numerical evidence that Rose is making Chicago better in ways that aren't being detected in his box score numbers.

This morning, Zach Lowe of SI.com's must-read Point Forward blog emailed me wondering how Utah's collapse in defensive rebounding % ranks among all-time declines. That got me wondering about the biggest drop-offs in all of the Four Factors, so I ran Z-scores on each team's numbers and looked at the biggest negative changes from one year to the next:

I finally got around to calculating the standard errors for our team Simple Ratings today:

Team

Estimate

Std. Error

SAS

7.97

2.62

MIA

6.90

2.60

BOS

6.67

2.63

LAL

5.78

2.59

CHI

4.81

2.61

ORL

4.61

2.61

DEN

3.48

2.63

DAL

3.30

2.62

NOH

2.40

2.60

OKC

2.05

2.61

ATL

1.75

2.60

UTA

1.73

2.61

HOU

0.86

2.60

POR

0.52

2.60

MEM

0.49

2.61

NYK

0.09

2.62

MIL

-0.57

2.65

PHI

-0.79

2.63

IND

-0.87

2.65

LAC

-1.51

2.63

PHO

-1.91

2.64

GSW

-2.92

2.62

CHA

-3.74

2.64

DET

-3.94

2.61

TOR

-4.23

2.62

MIN

-5.33

2.60

WAS

-5.82

2.64

SAC

-6.12

2.64

NJN

-6.22

2.61

CLE

-10.88

2.62

Then I set up a little Monte Carlo sim to estimate what is the probability of each team being the NBA's best (aka the team with the greatest "true" SRS skill). After 10,000 simulations using the estimates and standard errors above, here were the results:

What was the best run ever for your favorite team? What was the worst stretch of seasons? Let's take a look at the raw numbers in terms of NBA winning percentage over an x-year span (including our regressed 2011 WPcts):

Dogg looked at the top shot-blockers in the NBA by blocks per game, and did some digging through play-by-play data to determine how often the blocker's own team recovered the ball immediately after the swat (this is basically the first half of the "Bill Russell stat" Simmons talks about in The Book of Basketball). For instance, 67% of the time league leader Andrew Bogut blocks a shot, the Bucks end up with possession; compare that rate to 44% for Pau Gasol at the low end of the spectrum.

Some advanced stats underrate Anthony because they assume a quality shot can be created at will, every time down the floor. The logic is that if Anthony (an inefficient scorer) doesn't shoot, the team will just find someone else who can convert at a similar rate. And since Anthony isn't the most complete player in the world when you look beyond his scoring, it stands to reason that formulas which undervalue shot creation will see little reason to pay him top dollar.

But as Nate argues, Anthony is making his teammates better by taking the pressure to create off of them. His skills allow a team to surround him with defense-minded, low-usage players that compliment him, setting up something of a division of labor on the court. Silver lends credibility to this notion by showing that when players play alongside Carmelo, their offensive efficiencies increase.

I tend to agree with Silver's premise. This is why I constantly harp on "skill curves" and usage-efficiency tradeoffs, and why offensive statistical plus-minus contains a squared term for true shooting attempts per minute -- because there's a great deal of evidence that the marginal cost of possession usage declines as a player's offensive role increases. Unlike baseball, where "usage" is evenly spread out across all players and the only concern is an efficiency metric like OPS, the ability to create "at bats" is an important consideration.

In that way, Carmelo Anthony is just the latest in a long line of players who have been confounding statistical analysts for decades (before him, it was Allen Iverson). But as Silver, Kevin Pelton, and Henry Abbott are noting this week, one measuring stick for the evolution of basketball analysis is precisely how it deals with players like Anthony. I can't say he'd be the best fit for the Knicks specifically (New York -- 7th in offense, 23rd in defense, & featuring a player who already commands 31% of possessions -- seems a curious destination for an offense-only gunner), but in general it's useful to recognize his offensive value beyond pure efficiency metrics.

Thanks to some great research done by John Grasso that was posted on the APBR's website, the player pages now have debut dates for almost every player in NBA history. I was able to fill in debut dates for any player who debuted during the 1986-87 season or after, but John's work was a tremendous help in filling in the rest of the blanks.